The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

Yaladanda, Nikhila; Mopuri, Rajasekhar; Vavilala, Hariprasad; Bhimala, Kantha Rao; Gouda, K. C.; Kadiri, Madhusudhan Rao; Upadhyayula, Suryanarayana Murty; Mutheneni, Srinivasa Rao

doi:10.1007/s11356-023-26672-4

Cited by 2 publications

(1 citation statement)

References 67 publications

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“…39 In the first step of 10-fold crossvalidation, each Super Learner weighted seven candidate algorithms according to their prediction performance defined by the area under the receiver-operatorcharacteristic curve (AUC) (weights and descriptions of each algorithm are in the online supplemental table 4). Algorithms in R included gam, 40 biglasso, 41 xgboost, 42 glm and glm.interaction, 43 bayesglm 44 and ranger. 45 We used the default parameters in R for each candidate algorithm.…”

Section: Prediction Modelmentioning

confidence: 99%

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

et al. 2023

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ObjectiveSince rapid population growth challenges longitudinal population-based HIV cohorts in Africa to maintain coverage of their target populations, this study evaluated whether the exclusion of some residents due to growing population size biases key HIV metrics like prevalence and population-level viremia.Design, setting and participantsData were obtained from the Rakai Community Cohort Study (RCCS) in south central Uganda, an open population-based cohort which began excluding some residents of newly constructed household structures within its surveillance boundaries in 2008. The study includes adults aged 15–49 years who were censused from 2019 to 2020.MeasuresWe fit ensemble machine learning models to RCCS census and survey data to predict HIV seroprevalence and viremia (prevalence of those with viral load >1000 copies/mL) in the excluded population and evaluated whether their inclusion would change overall estimates.ResultsOf the 24 729 census-eligible residents, 2920 (12%) residents were excluded from the RCCS because they were living in new households. The predicted seroprevalence for these excluded residents was 10.8% (95% CI: 9.6% to 11.8%)—somewhat lower than 11.7% (95% CI: 11.2% to 12.3%) in the observed sample. Predicted seroprevalence for younger excluded residents aged 15–24 years was 4.9% (95% CI: 3.6% to 6.1%)—significantly higher than that in the observed sample for the same age group (2.6% (95% CI: 2.2% to 3.1%)), while predicted seroprevalence for older excluded residents aged 25–49 years was 15.0% (95% CI: 13.3% to 16.4%)—significantly lower than their counterparts in the observed sample (17.2% (95% CI: 16.4% to 18.1%)). Over all ages, the predicted prevalence of viremia in excluded residents (3.7% (95% CI: 3.0% to 4.5%)) was significantly higher than that in the observed sample (1.7% (95% CI: 1.5% to 1.9%)), resulting in a higher overall population-level viremia estimate of 2.1% (95% CI: 1.8% to 2.4%).ConclusionsExclusion of residents in new households may modestly bias HIV viremia estimates and some age-specific seroprevalence estimates in the RCCS. Overall, HIV seroprevalence estimates were not significantly affected.

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Section: Prediction Modelmentioning

confidence: 99%

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

et al. 2023

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New insight into avian malaria vectors in New Zealand

Schoener,

Tompkins,

Howe

et al. 2024

Parasites Vectors

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Background Mosquitoes (Culicidae) are vectors for most malaria parasites of the Plasmodium species and are required for Plasmodium spp. to complete their life cycle. Despite having 16 species of mosquitoes and the detection of many Plasmodium species in birds, little is known about the role of different mosquito species in the avian malaria life cycle in New Zealand. Methods In this study, we used nested polymerase chain reaction (PCR) and real-time PCR to determine Plasmodium spp. prevalence and diversity of mitochondrial cytochrome b gene sequences in wild-caught mosquitoes sampled across ten sites on the North Island of New Zealand during 2012–2014. The mosquitoes were pooled by species and location collected, and the thorax and abdomens were examined separately for Plasmodium spp. DNA. Akaike information criterion (AIC) modeling was used to test whether location, year of sampling, and mosquito species were significant predictors of minimum infection rates (MIR). Results We collected 788 unengorged mosquitoes of six species, both native and introduced. The most frequently caught mosquito species were the introduced Aedes notoscriptus and the native Culex pervigilans. Plasmodium sp DNA was detected in 37% of matched thorax and abdomen pools. When considered separately, 33% of abdomen and 23% of thorax pools tested positive by nested PCR. The MIR of the positive thorax pools from introduced mosquito species was 1.79% for Ae. notoscriptus and 0% for Cx. quinquefasciatus, while the MIR for the positive thorax pools of native mosquito species was 4.9% for Cx. pervigilans and 0% for Opifex fuscus. For the overall MIR, site and mosquito species were significant predictors of Plasmodium overall MIR. Aedes notoscriptus and Cx. pervigilans were positive for malaria DNA in the thorax samples, indicating that they may play a role as avian malaria vectors. Four different Plasmodium lineages (SYAT05, LINN1, GRW6, and a new lineage of P (Haemamoeba) sp. AENOT11) were identified in the pooled samples. Conclusions This is the first detection of avian Plasmodium DNA extracted from thoraxes of native Culex and introduced Aedes mosquito species in New Zealand and therefore the first study providing an indication of potential vectors in this country. Graphical Abstract

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The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

Cited by 2 publications

References 67 publications

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

New insight into avian malaria vectors in New Zealand

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